Model Calibration, Bayesian Inference & MCMC — Hands-on

Learn how to calibrate systems biology models using Bayesian inference and Markov Chain Monte Carlo. This module introduces the calibration mindset, likelihood and priors, Bayesian updating, and practical MCMC workflows so that you can quantify parameter uncertainty and generate decision ready predictions.

Model Calibration, Bayesian Inference & MCMC

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Session Index

Session 1 — Calibration Mindset, Likelihood & Priors Session 2 — Bayesian Inference Basics & Posterior Thinking Session 3 — MCMC Samplers, Workflows & Uncertainty Use Session 4 — Mini Capstone: Bayesian Calibration of a Small Model

Session 1

Fee: Rs 8800

Calibration Mindset, Likelihood & Priors

What calibration means for systems models

parameters as uncertain quantities data, model and discrepancy view fitting vs calibration mindset

Likelihood intuition for experimental data

linking model outputs to observations simple noise models idea goodness of fit and residual thinking

Priors and information sources for parameters

literature and expert knowledge wide vs informative priors constraints and scaling ideas

Session 2

Fee: Rs 11800

Bayesian Inference Basics & Posterior Thinking

Bayesian updating conceptually

prior, likelihood and posterior evidence as a normalizing term posterior as updated belief

Posterior summaries and intervals

means and medians for parameters credible intervals intuition joint vs marginal views

Posterior predictive thinking

simulating new data from posterior checking model fit conceptually prediction intervals vs parameter intervals

Session 3

Fee: Rs 14800

MCMC Samplers, Workflows & Uncertainty Use

MCMC idea for exploring posteriors

Markov chains and random walks Metropolis Hastings concept Gibbs and gradient based ideas (high level)

Convergence and diagnostics intuition

trace plots and mixing multiple chains and R hat idea effective sample size awareness

Using uncertainty for decisions and design

probabilistic statements on parameters prediction bands for trajectories prioritizing new experiments conceptually

Session 4

Fee: Rs 18800

Mini Capstone: Bayesian Calibration of a Small Model

Choose a small dynamic model and data example

theory plus guided practical

Set priors, define likelihood and run a simple MCMC workflow

parameter and data setup run chains and check diagnostics summarize posterior and predictions

Deliverables: notebook, posterior plots and short note

Python or R notebook histograms and trace plots PDF or HTML project summary

PDF

Model Calibration, Bayesian Inference and MCMC Training | Uncertainty Aware Model Fitting

Model Calibration, Bayesian Inference & MCMC — Hands-on